Generally speaking, cellular communication systems offer communication channels to multiple users within a given service area, e.g., cell, at the same time. Such communication channels include an uplink, i.e., a mobile terminal to base station communication channel, and a downlink, i.e., a base station to mobile terminal communication channel, to facilitate two-way, multiple access communication with a number of users. Regardless of which multiple access communication scheme is employed, however, the number of users that are serviceable in a given cell is bounded by an upper limit.
In a Time Division Multiple Access (TDMA) system, for example, the number of users that may be accommodated by the respective cell is bounded by the number of timeslots, M, that are available within the uplink and downlink frequency bands. Such frequency bands may be represented as contiguous time-frequency planes, where M timeslots are available within the time-frequency plane. For example, the number of mobile terminals able to simultaneously communicate with their respective base stations is equal to M, whereby the Mth user transmits signal energy in the Mth timeslot of the uplink using a low duty cycle. Receptions from the base station to the mobile terminal are similarly bounded in the downlink.
In a Code Division Multiple Access (CDMA) system, on the other hand, the signal energy is continuously distributed throughout the entire time-frequency plane, whereby each user shares the entire time-frequency plane by employing a wideband coded signaling waveform. Thus, the number of users that may be simultaneously accommodated in a CDMA system is not bounded by the number of timeslots available within the time-frequency plane, but is rather a function of the number of users present within the communication channel and the amount of Processing Gain (PG) employed by the CDMA system. The PG of a CDMA system is defined to be the ratio of the bandwidth of the spread signal in Hertz (Hz) to the data signal bandwidth in Hz.
The number of users transmitting within a given CDMA channel contributes to the total amount of undesired signal power received and is thus a measure of the jamming signal power resulting from multiple access users within the CDMA channel. Thus, depending upon the PG and jamming signal power present at the CDMA receiver, an upper limit may be calculated for the number of users that may be supported by a given CDMA channel.
For example, if the information bandwidth of the data signal to be transmitted is 9600 Hz and the transmission bandwidth of the data signal is 1.152 Megahertz (Mhz), then the PG=1152000/9600=120, or 20.8 decibels (dB). Furthermore, if the required bit energy-to-noise spectral density ratio (Eb/N0) for acceptable performance of the CDMA communication system is equal to 6 dB, then the communicator can achieve its objective even in the presence of jamming signal power in excess of 14.8 dB. That is to say, that the jamming margin tolerated by the receiver is calculated to be 20.8−6=14.8 dB. Thus, if every user in the spread spectrum bandwidth supplies the identical amount of signal power to the base station antenna through a perfect power control scheme, regardless of location, then 102.08=120 Multiple Access (MA) users may be accommodated by that CDMA channel.
The idea of a CDMA communication system, therefore, is to expend the jamming margin by accommodating the maximal number of co-channel communicators possible. As mentioned above, these co-channel communicators occupy the frequency-time plane simultaneously and thus account for the interference, or jamming power as seen at the CDMA receiver. In theory, Multiple Access Interference (MAI) caused by MA users within the CDMA channel can be reduced to zero if their respective signals are mutually orthogonal. In practice, however, co-channel interference, or cross-correlation from other codes, is still present, since delayed and attenuated replicas of the signals that arrive non-synchronously are not orthogonal to their primary components. Similarly, signals received from neighboring cells contribute to the MAI, since those signals are non-synchronous, and thus are not orthogonal to signals received from the home cell.
A conventional CDMA receiver demodulates each user's signal as if it were the only signal present by using a bank of filters that are matched to the user's signal waveform. Since the user's signal also contains cross-correlation from other codes, i.e., interference, the matched filters exhibit increasingly poor performance as the number of users increases, or as the relative power of the interference signals becomes large. Thus, it is imperative that the receiver be capable of determining which of N possible messages is the transmitted message in the presence of this interference.
It is well known that the Maximum Likelihood (ML) sequence detector, which is based on the maximum a posteriori probability (MAP) receiver principle, is the optimal receiver for performing such determinations in the presence of interference. The complexity of the ML sequence detector, however, is exponentially related to the number of codes being processed, which yields prohibitively challenging computational and storage implementations.
Prior art attempts to achieve a good trade-off between performance and complexity have spawned a number of Multi-User Detection (MUD) research activities. Among these, the multi-stage Parallel Interference Cancellation (PIC) technique, presents a promising algorithm for real time implementation because of its relatively low computational complexity and good performance. In particular, the Complete-PIC and the Partial-PIC algorithms have received attention in the literature.
Complete-PIC is a subtractive interference cancellation scheme that assumes that the symbol detection from a previous stage is correct. An MAI estimate is then made from the previous stage detection, which is then completely subtracted from the received signal. If some of the symbol detection is wrong, e.g., when the system load is high or the iteration is in its early stages, an erroneous interference estimate results, which when subtracted from the received signal may introduce even more interference than had previously existed. This phenomenon leads to the so-called “ping-pong” effect in the conventional Complete-PIC scheme.
In such situations, it is not preferable to cancel the entire estimated interference. Thus, a partial cancellation, i.e., Partial-PIC, of the MAI may be performed by introducing a weight in each stage. The weights are found by trial and error with the constraint that the value of each weight takes on values between 0 and 1. Although considerable capacity enhancement over the Complete-PIC algorithm is achieved by Partial-PIC, it is known that the choice of the weights used in each stage affects the performance significantly. Thus, incorrect selection of the weights has less than acceptable performance characteristics.
While MAI reduction techniques continue to develop, very few research activities have studied the viability of Very Large Scale Integration (VLSI) implementation of these techniques. While the Complete-PIC and Partial-PIC algorithms provide good performance with relatively low computational complexity, their real-time hardware implementations are still extremely challenging. Commercialization of these algorithms is particularly dependent upon finding a viable VLSI architecture that can apply the hardware resources efficiently to achieve low power and low cost in its design.
Accordingly, there is a need in the communications industry for an MAI reduction algorithm that further reduces computational complexity over existing techniques. In addition, the reduced computational complexity should compliment its VLSI implementation by utilizing features inherent with the MAI reduction algorithm. The present invention fulfills these and other needs, and offers other advantages over the prior art MAI reduction approaches.